Registration algorithms, such as Iterative Closest Point (ICP), have proven effective in mobile robot localization algorithms over the last decades. However, they are susceptible to failure when a robot sustains extreme velocities and accelerations. For example, this kind of motion can happen after a collision, causing a point cloud to be heavily skewed. While point cloud de-skewing methods have been explored in the past to increase localization and mapping accuracy, these methods still rely on highly accurate odometry systems or ideal navigation conditions. In this paper, we present a method taking into account the remaining motion uncertainties of the trajectory used to de-skew a point cloud along with the environment geometry to increase the robustness of current registration algorithms. We compare our method to three other solutions in a test bench producing 3D maps with peak accelerations of 200 m/s^2 and 800 rad/s^2. In these extreme scenarios, we demonstrate that our method decreases the error by 9.26 % in translation and by 21.84 % in rotation. The proposed method is generic enough to be integrated to many variants of weighted ICP without adaptation and supports localization robustness in harsher terrains.

中文翻译：

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激光雷达扫描配准对极端运动具有鲁棒性

在过去的几十年中，诸如迭代最近点（ICP）之类的配准算法在移动机器人本地化算法中被证明是有效的。但是，当机器人承受极高的速度和加速度时，它们很容易发生故障。例如，这种运动可能在碰撞后发生，从而导致点云严重倾斜。虽然过去已经探索了点云解偏方法来提高定位和制图精度，但是这些方法仍然依赖于高精度的测距系统或理想的导航条件。在本文中，我们提出了一种方法，该方法考虑了用于消除点云偏斜的轨迹的剩余运动不确定性以及环境几何形状，以提高当前配准算法的鲁棒性。我们将我们的方法与其他三个解决方案进行了比较，在测试台上生成了3D映射，其峰值加速度分别为200 m / s ^ 2和800 rad / s ^ 2。在这些极端情况下，我们证明了我们的方法在平移时减少了9.26％的误差，在旋转时减少了21.84％的误差。所提出的方法足够通用，无需集成即可集成到加权ICP的许多变体中，并支持在更苛刻地形中的定位鲁棒性。